Filtering Method Research Articles

A comparative study of bandpass-filter-based multi-scale methods for turbulence energy cascade

Two different bandpass filtering methods have been used in the literature to study turbulence energy cascade simultaneously in physical and scale spaces. One of them is the derivative-based filter by Leung et al. [J Fluid Mech. 2012;V710:453–481] and the other is the difference-based filter by Motoori and Goto [J Fluid Mech. 2021;V911:A27]. The differences in their definitions lead to a variability in the quantitative results across the literature. In this work, we compare the two filtering methods and show that the difference-based filter can be related to the derivative-based filter through Taylor's approximation. Within the truncation error, the deviation between the two filters depends on the ‘filter width’, ‘a’, which is an input to the difference-based filter. The apparent reason for the deviations is that for large ‘a’, the difference-based filter extracts scales in a wider bandwidth compared to the derivative-based filter, which corresponds to the limiting case of the difference-based filter as a → 1 . Thus, one must be careful while choosing a filter based on their objectives, and while comparing results based on different filter designs. It is hoped that the present work will help reconcile the quantitative variations across the relevant literature.

Methodological assessment for efficient collection of Schistosoma mansoni environmental DNA and improved schistosomiasis surveillance in tropical wetlands

Schistosomiasis, caused by trematodes of genus Schistosoma, is among the most seriously neglected tropical diseases. Although rapid surveillance of risk areas for Schistosoma transmission is vital to control schistosomiasis, the habitat and infection status of this parasite are difficult to assess. Environmental DNA (eDNA) analysis, involving the detection of extra-organismal DNA in water samples, facilitates cost-efficient and sensitive biomonitoring of aquatic environments and is a promising tool to identify Schistosoma habitat and infection risk areas. However, in tropical wetlands, highly turbid water causes filter clogging, thereby decreasing the filtration volume and increasing the risk of false negatives. Therefore, in this study, we aimed to conduct laboratory experiments and field surveys in Lake Victoria, Mbita, to determine the appropriate filter pore size for S. mansoni eDNA collection in terms of particle size and filtration volume. In the laboratory experiment, aquarium water was sequentially filtered using different pore size filters. Targeting >3 µm size fraction was found to be sufficient to capture S. mansoni eDNA particles, regardless of their life cycle stage (egg, miracidia, and cercaria). In the field surveys, GF/D (2.7 µm nominal pore size) filter yielded 2.5-times the filtration volume obtained with a smaller pore size filter and pre-filtration methods under the same time constraints. Moreover, a site-occupancy model was applied to the field detection results to estimate S. mansoni eDNA occurrence and detection probabilities and assess the number of water samples and PCR replicates necessary for efficient eDNA detection. Overall, this study reveals an effective method for S. mansoni eDNA detection in turbid water, facilitating the rapid and sensitive monitoring of its distribution and cost-effective identification of schistosomiasis transmission risk areas.

Dynamic features and wind-resistant strategy of suspended monorail vehicle-track beam systems subjected to turbulent wind

Suspended monorail vehicles (SMV), hung beneath box beams with open bottoms, exhibit significant transverse wobble when exposed to turbulent winds, markedly impacting passenger comfort. This study presents an effective methodology for comprehensively evaluating the dynamical behavior of suspended monorail vehicle-track beam systems (SMVTBS) under wind action. First, based on the multi-rigid body dynamics theory and finite element method, an integrated vehicle-track beam interaction model is established. Then, by applying the auto-regressive (AR) linear filtering method, a turbulent wind velocity field with inherent correlations is adequately simulated through a stochastic process, which is later converted into wind force acting on both the track beam and vehicles. On this basis, the vibration responses of the SMVTBS with and without wind actions are compared, and the effects of the wind load on the vehicle-track beam dynamical behavior are revealed. Furthermore, to improve the wind-resistant performance of the SMV, several crucial design parameters are reasonably selected and effective wind-resistant strategies are proposed. Finally, the dynamic performance of the SMV is evaluated under wind action, and suitable train running speeds were recommended at different wind velocities to guarantee vehicle riding comfort. The research results can provide useful guidance for the design and operation of SMVTBS against crosswinds.

Research on denoising method based on temperature and humidity profile lidar

Due to the fact that the vibration and pure rotational Raman signals collected by the temperature and humidity profile lidar were 3–4 orders of magnitude weaker than the Mie scattering signal, they were susceptible to electronic and white noise interference, which seriously affected the system signal-to-noise ratio. In this paper, an improved VMD-WT filtering method was adopted to extract effective signals and denoise. The processing outcome of several filtering algorithms was evaluated, and noisy signals were simulated to confirm the algorithm's efficacy. Based on the quantitative computation of evaluation indicators, such as signal-to-noise ratio, root mean square error, and correlation, the improved VMD-WT algorithm had more significant advantages in indicators such as signal-to-noise ratio. In order to further verify the robustness and adaptability of the proposed algorithm, experimental analysis of the filtering algorithm was conducted on the continuously collected temperature and humidity measured signals. The results demonstrated that the algorithm not only improved the detection range of lidar and suppressed high-altitude noise effectively, but also performed well in processing strong interference signals, like clouds, which led to a significant improvement in the atmospheric optical parameter inversion results. Furthermore, pseudo-color images of aerosols, temperature, and humidity changes over time and space have been used to further illustrate the algorithm's dependability and wide range of potential uses.

Combining the digital filtering method with the SWAT model to simulate spatiotemporal variations of baseflow in a mountainous river basin

Study regionGanjiang River Basin, a typical mountainous river basin which located on the south bank of the middle-lower Yangtze River. The Ganjiang River is the seventh largest tributary of the Yangtze River. Study focusBaseflow, a key recharge source for the river streamflow. This study combined the digital filtering method with the SWAT model to examine the temporal and spatial patterns of baseflow across the Ganjiang River Basin, and quantitatively assessed land use change impact on baseflow. New hydrological insights for the regionBaseflow in the Ganjiang River Basin shows a "single peak" intra-annual distribution. Monthly variations of streamflow and baseflow across the basin are different. The variation of baseflow index is generally opposite to that of streamflow. A positive correlation has been noted between the annual baseflow and streamflow, while a negative correlation was found between the annual baseflow index and precipitation. Due to the potential influence of basin topography, river flow direction and rock layer distribution, baseflow and baseflow modulus showed a spatially increasing trend from south to north, with the northwest region having extremely strong groundwater recharge. In comparison to the basic scenario, under extreme land use scenarios of forest, grassland, and cropland, baseflow may experience an increase of 14.7 % and 2.9 %, while witness a decrease of 13.9 %. All results improve the understanding of baseflow spatiotemporal variations in river basins.

Wavelet denoising analysis on vacuum-process monitoring signals of aerospace vacuum vessel structures

Abstract The monitoring of micro-defects or external actions induced vacuum degradation in aerospace vacuum vessels is an important challenge. A vacuum-process monitoring method based on quasi-distributed fiber Bragg grating (FBG) sensing technology is proposed. Due to the influence of environmental noise and vacuum pump operation noise, the raw signals measured by FBG sensors contain a large amount of noises, which affects the measurement accuracy and data analysis. Therefore, the wavelet threshold (WT) denoising method is proposed to analyze the influence of noises on the monitoring signals measured by two different kinds of FBG sensors. The evaluation of the monitoring signals after denoising indicates that the proposed method can effectively remove the noise and significantly improve signal quality. The highest signal-to-noise ratio of the processed signals can reach 37.61 dB and the mean square error is 3.68 × 10−7, while retaining the key features of the original signal. The proposed WT denoising method demonstrates better performance and feasibility compared with moving average filtering and Kalman filtering methods. The study provides critical supports for improving the performance and reliability of the vacuum vessel monitoring system.

An Artificial Neural Network Prediction Model of Depressive Symptoms among Women with Abnormal Papanicolaou Smear Results before and after Diagnostic Procedures.

(1) Background: Cervical screening and additional diagnostic procedures often lead to depression. This research aimed to develop a prediction model for depression in women who received an abnormal Papanicolaou screening test, prior to and following the diagnostic procedures. (2) Methods: The study included women who had a positive Papanicolaou screening test (N = 172) and attended the Clinical Center of Kragujevac in Serbia for additional diagnostic procedures (colposcopy/biopsy/endocervical curettage). Women filled out a sociodemographic survey and the Center for Epidemiologic Studies Depression questionnaire (CES-D scale) before and after diagnostic procedures. A prediction model was built with multilayer perceptron neural networks. (3) Results: A correlation-based filter method of feature selection indicated four variables that correlated with depression both prior to and following the diagnostic procedures-anxiety, depression, worry, and concern about health consequences. In addition, the use of sedatives and a history of both induced and spontaneous abortion correlated with pre-diagnostic depression. Important attributes for predicting post-diagnostic depression were scores for the domains 'Tension/discomfort' and 'Embarrassment' and depression in personal medical history. The accuracy of the pre-diagnostic procedures model was 70.6%, and the area under the receiver operating characteristic curve (AUROC) was 0.668. The model for post-diagnostic depression prediction showed an accuracy of 70.6%, and an AUROC = 0.836. (4) Conclusions: This study helps provide means to predict the occurrence of depression in women with an abnormal Papanicolaou screening result prior to and following diagnostic procedures, which can aid healthcare professionals in successfully providing timely psychological support to those women who are referred to further diagnostics.

Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER.

Recent technological developments in single-cell RNA-seq CRISPR screens enable high-throughput investigation of the genome. Through transduction of a gRNA library to a cell population followed by transcriptomic profiling by scRNA-seq, it is possible to characterize the effects of thousands of genomic perturbations on global gene expression. A major source of noise in scRNA-seq CRISPR screens are ambient gRNAs, which are contaminating gRNAs that likely originate from other cells. If not properly filtered, ambient gRNAs can result in an excess of false positive gRNA assignments. Here, we utilize CRISPR barnyard assays to characterize ambient gRNA noise in single-cell CRISPR screens. We use these datasets to develop and train CLEANSER, a mixture model that identifies and filters ambient gRNA noise. This model takes advantage of the bimodal distribution between native and ambient gRNAs and includes both gRNA and cell-specific normalization parameters, correcting for confounding technical factors that affect individual gRNAs and cells. The output of CLEANSER is the probability that a gRNA-cell assignment is in the native distribution over the ambient distribution. We find that ambient gRNA filtering methods impact differential gene expression analysis outcomes and that CLEANSER outperforms alternate approaches by increasing gRNA-cell assignment accuracy.

PYALLFFS: An Open-Source Library for All Filter Feature Selection Methods

Feature selection is a significant data mining and machine learning technique that enhances model performance by identifying important features within a dataset, reducing the risk of overfitting while aiding the model in making faster and more accurate predictions. pyallffs is a Python library developed to optimize the feature selection process, offering rich content and low dependency requirements. With 19 different filtering methods, pyallffs assists in analyzing dataset features to determine the most relevant ones. Users can apply custom filtering methods to their datasets using pyallffs, thereby achieving faster and more effective results in data analytics and machine learning projects. The source codes, supplementary materials, and guidance is publicly available on GitHub: https://github.com/tohid-yusefi/pyallffs

Speckle reduction in digital holography with Non-local means filter based on the structural similarity

Speckle noise limits the application of digital holography across various fields. This paper proposes a Non-local means filter method to suppress speckle noise by determining the weight of each traversed pixel based on the structural similarity between image blocks centered on the target and traversed pixels. Experimental results show that, compared to other typical digital image processing methods, this method can significantly reduce the speckle noise and exhibits a clear advantage across various metrics. These results indicate that the proposed method holds significant development potential in the field of speckle noise reduction.

Analysis of the Spatial Distribution and Common Mode Error Correlation in a Small-Scale GNSS Network.

When analyzing GPS time series, common mode errors (CME) often obscure the actual crustal movement signals, leading to deviations in the velocity estimates of station coordinates. Therefore, mitigating the impact of CME on station positioning accuracy is crucial to ensuring the precision and reliability of GNSS time series. The current approach to separating CME mainly uses signal filtering methods to decompose the residuals of the observation network into multiple signals, from which the signals corresponding to CME are identified and separated. However, this method overlooks the spatial correlation of the stations. In this paper, we improved the Independent Component Analysis (ICA) method by introducing correlation coefficients as weighting factors, allowing for more accurate emphasis or attenuation of the contributions of the GNSS network's spatial distribution during the ICA process. The results show that the improved Weighted Independent Component Analysis (WICA) method can reduce the root mean square (RMS) of the coordinate time series by an average of 27.96%, 15.23%, and 28.33% in the E, N, and U components, respectively. Compared to the ICA method, considering the spatial distribution correlation of stations, the improved WICA method shows enhancements of 12.53%, 3.70%, and 8.97% in the E, N, and U directions, respectively. This demonstrates the effectiveness of the WICA method in separating CMEs and provides a new algorithmic approach for CME separation methods.

State of Charge Estimation of Lithium-Ion Batteries Based on Fractional-Order Model with Mul-ti-Innovations Dual Cubature Kalman Filter Method

An accurate estimation of the lithium battery’s state of charge (SOC) is critical. The article proposes a dual fractional order multi-innovations cubature Kalman filter (DFOMICKF) algorithm for estimating lithium battery SOC. The algorithm adopts the idea of multiple time scales, where one of the FOMICKF is used to identify the circuit model parameters online in the macro time scale. Another FOMICKF is used to estimate the SOC in the micro time scale, and the circuit parameters updated online in real-time are passed into the estimation of the SOC filter to form an online joint estimation method of SOC and circuit parameters. Finally, multiple algorithms of DFOMICKF, FOMICKF, FOCKF, and CKF are compared and experimented under different working conditions to compare and analyze the estimated SOC errors. It is verified that the proposed algorithm can solve the problems of inaccuracy, poor convergence, and poor robustness of the traditional Kalman filtering algorithm for estimating SOC, which has good research value.

Trajectory-aware privacy-preserving method with local differential privacy in crowdsourcing

In spatial crowdsourcing services, the trajectories of the workers are sent to a central server to provide more personalized services. However, for the honest-but-curious servers, it also poses a challenge in terms of potential privacy leakage of the workers. Local differential privacy (LDP) is currently the latest technique to protect data privacy. However, most of LDP-based schemes have limitations in providing good utility due to extensive noise in perturbing trajectories. In this work, to balance the privacy and utility, we propose a novel pattern-aware privacy protection method called trajectory-aware privacy-preserving with local differential privacy (TALDP). The key idea is that, rather than applying the same degree of perturbation to all location points, we employ adaptive privacy budget allocation, assigning varied privacy budgets to individual location points, thereby mitigating the perturbation’s impact and enhancing overall utility. Meanwhile, to ensure the privacy, we give the different perturbing points to different privacy budgets according to their important degree for the patterns of the trajectories. In particular, we use Karman filter method to select the important location points and decide their privacy budgets. We conduct extensive experiments on three real datasets. The results show that our approach improves the utility over many other current methods while still provide good the privacy protection.

Effect of impact kinematic filters on brain strain responses in contact sports.

Impact kinematics are widely employed to investigate mechanisms of traumatic brain injury (TBI). However, they are susceptible to noise and artefacts; thus, require data filtering. Few studies have focused on how data filtering affects brain strain most relevant to TBI. Here, we report that impact-induced brain strains are much less sensitive to data filtering than kinematics based on three filtering methods: CFC180, lowpass 200Hz, and a new method called Head Exposure to Acceleration Database in Sport (HEADSport). Using mouthguard-measured head impacts in elite rugby (N=5694), average Euclidean distances between the three filtered angular velocity profiles and their unfiltered counterparts are used to identify three groups of impacts with large variations: 90-95th, 95-99th, and >99th percentile. From each group, 20 impacts are randomly selected for simulation using the anisotropic Worcester Head Injury Model (WHIM) V1.0. HEADSport and CFC180 are the most and least effective, respectively, in suppressing "unphysical artefacts" shown as sharp spikes with a rather short impulse duration (e.g., <3 ms) in angular velocity. However, maximum principal strain (MPS), especially that in the corpus callosum, is much less sensitive to data filtering compared to kinematic peaks (e.g., reduction of 3% vs. 47% and 90% for peak angular velocity and acceleration with HEADSport for impacts of >99th percentile). These findings confirm that the brain acts as a low-pass filter, itself, to suppress high frequency noise in impact kinematics. Therefore, brain strain can serve as a common metric for TBI biomechanical studies to maximize relevance to the injury, as it is not sensitive to kinematic filters.

Groundwater and indoor air quality of urban village housing: study in Janturan, Warungboto, Yogyakarta

Water and air are important elements for life. The presence of pathogenic bacteria in the surrounding environment can cause health problems resulting from the low quality of water and air. The purpose of this study is to measure the quality of groundwater and air based on microbiological parameters in urban village housing. This research was conducted in a descriptive survey, and a purposive sampling technique was carried out. Groundwater and air samples were taken from six residents' houses located in the Janturan area. The water quality test used the most probable number (MPN) method and the air quality test used the agar filter method using a microbiological air sampler (MAS) to trap microbes. The results of the MPN test for total coliform bacteria from six samples of groundwater showed that three samples were positive for coliform bacteria which exceeded the threshold according to Indonesian standard&amp;lt;50 MPN/100 ml. Assay of &lt;em&gt;E. coli&lt;/em&gt; bacteria showed that all samples of groundwater positively contained &lt;em&gt;E. coli&lt;/em&gt; bacteria which exceeded the threshold of 0 MPN/100 ml. The results of indoor air quality measurements from 6 residents' houses showed that five houses have air quality did not meet the standard for indoor air germ numbers based on Indonesian standard, less than 700 CFU/m3 (colony forming units per metres cubic) and 1 house that got the number of airborne germs, in the normal range, namely 356 CFU/m3. It was concluded that the water quality and air quality in the urban village did not meet health requirements.

The Turreted Gun System Technology Integrated To The Helmet Mounted Display System

This research aims to emphasize the operational importance of avionics subsystems integrated into helicopters, primarily used for assault purposes, such as the Helmet Mounted Display System (HMDS) and Turreted Gun Systems. It addresses the functional capabilities of HMDS in target detection and aiming, as well as the defense and engagement capabilities of Turreted Gun Systems against various threats. The focus of the research is on evaluating the parametric data, such as the dispersion of the Turreted Gun Systems and the targeting performance values of the HMDS, during their use together. It also assesses the transmission of this data through a Kalman filter before being sent to the Turreted Gun Systems to mitigate platform-induced disruptive factors. The findings help us understand the profound impact of technological advancements in helicopter avionics systems on operational effectiveness. Additionally, this study sheds light on the impact of dispersion values on hit performance during usage. It is assessed that the proposed method in this study will result in more stable data, and that controlling the Turreted Gun Systems with this stable data can enhance hit performance. The study can be further developed by examining subsystems with different performance characteristics and filtering methods.

A hybrid approach for efficient feature selection in anomaly intrusion detection for IoT networks

The exponential growth of Internet of Things (IoT) devices underscores the need for robust security measures against cyber-attacks. Extensive research in the IoT security community has centered on effective traffic detection models, with a particular focus on anomaly intrusion detection systems (AIDS). This paper specifically addresses the preprocessing stage for IoT datasets and feature selection approaches to reduce the complexity of the data. The goal is to develop an efficient AIDS that strikes a balance between high accuracy and low detection time. To achieve this goal, we propose a hybrid feature selection approach that combines filter and wrapper methods. This approach is integrated into a two-level anomaly intrusion detection system. At level 1, our approach classifies network packets into normal or attack, with level 2 further classifying the attack to determine its specific category. One critical aspect we consider is the imbalance in these datasets, which is addressed using the Synthetic Minority Over-sampling Technique (SMOTE). To evaluate how the selected features affect the performance of the machine learning model across different algorithms, namely Decision Tree, Random Forest, Gaussian Naive Bayes, and k-Nearest Neighbor, we employ benchmark datasets: BoT-IoT, TON-IoT, and CIC-DDoS2019. Evaluation metrics encompass detection accuracy, precision, recall, and F1-score. Results indicate that the decision tree achieves high detection accuracy, ranging between 99.82 and 100%, with short detection times ranging between 0.02 and 0.15 s, outperforming existing AIDS architectures for IoT networks and establishing its superiority in achieving both accuracy and efficient detection times.

Adaptive polynomial Kalman filter for nonlinear state estimation in modified AR time series with fixed coefficients

AbstractThis article presents a novel approach for adaptive nonlinear state estimation in a modified autoregressive time series with fixed coefficients, leveraging an adaptive polynomial Kalman filter (APKF). The proposed APKF dynamically adjusts the evolving system dynamics by selecting an appropriate autoregressive time‐series model corresponding to the optimal polynomial order, based on the minimum residual error. This dynamic selection enhances the robustness of the state estimation process, ensuring accurate predictions, even in the presence of varying system complexities and noise. The proposed methodology involves predicting the next state using polynomial extrapolation. Extensive simulations were conducted to validate the performance of the APKF, demonstrating its superiority in accurately estimating the true system state compared with traditional Kalman filtering methods. The root‐mean‐square error was evaluated for various combinations of standard deviations of sensor noise and process noise for different sample sizes. On average, the root‐mean‐square error value, which represents the disparity between the true sensor reading and estimate derived from the adaptive Kalman filter, was 35.31% more accurate than that of the traditional Kalman filter. The comparative analysis highlights the efficacy of the APKF, showing significant improvements in state estimation accuracy and noise resilience.

A Novel Hierarchical Clustering Sequential Forward Feature Selection Method for Paddy Rice Agriculture Mapping Based on Time-Series Images

Timely and accurate mapping of rice distribution is crucial to estimate yield, optimize agriculture spatial patterns, and ensure global food security. Feature selection (FS) methods have significantly improved computational efficiency by reducing redundancy in spectral and temporal feature sets, playing a vital role in identifying and mapping paddy rice. However, the optimal feature sets selected by existing methods suffer from issues such as information redundancy or local optimality, limiting their accuracy in rice identification. Moreover, the effects of these FS methods on rice recognition in various machine learning classifiers and regions with different climatic conditions and planting structures is still unclear. To overcome these limitations, we conducted a comprehensive evaluation of the potential applications of major FS methods, including the wrapper method, embedded method, and filter method for rice mapping. A novel hierarchical lustering sequential forward selection (HCSFS) method for precisely extracting the optimal feature set for rice identification is proposed. The accuracy of the HCSFS and other FS methods for rice identification was tested with nine common machine learning classifiers. The results indicated that, among the three FS methods, the wrapper method achieved the best rice mapping performance, followed by the embedded method, and lastly, the filter method. The new HCSFS significantly reduced redundant features compared with eleven typical FS methods, demonstrating higher precision and stability, with user accuracy and producer accuracy exceeding 0.9548 and 0.9487, respectively. Additionally, the spatial distribution of rice maps generated using the optimal feature set selected by HCSFS closely aligned with actual planting patterns, markedly outperforming existing rice products. This research confirms the effectiveness and transferability of the HCSFS method for rice mapping across different climates and cultivation structures, suggesting its enormous potential for classifying other crops using time-series remote sensing images.

Explainable machine-learning-based prediction of QCT/FEA-calculated femoral strength under stance loading configuration using radiomics features.

Finite element analysis can provide precise femoral strength assessment. However, its modeling procedures were complex and time-consuming. This study aimed to develop a model to evaluate femoral strength calculated by quantitative computed tomography-based finite element analysis (QCT/FEA) under stance loading configuration, offering an effective, simple, and explainable method. One hundred participants with hip QCT images were selected from the Hong Kong part of the Osteoporotic fractures in men cohort. Radiomics features were extracted from QCT images. Filter method, Pearson correlation analysis, and least absolute shrinkage and selection operatormethod were employed for feature selection and dimension reduction. The remainingfeatures were utilized as inputs, and femoral strengths were calculated as the ground truth through QCT/FEA. Support vector regressionwas applied to develop a femoral strength prediction model. The influence of various numbers of input features on prediction performance was compared, and the femoral strength prediction model was established. Finally, Shapley additive explanation, accumulated local effects, and partial dependency plotmethods were used to explain the model. The results indicated that the model performed best when six radiomics features were selected. The coefficient of determination (R2), the root mean square error, the normalized root mean square error, and the mean squared erroron the testing set were 0.820, 1016.299 N, 10.645%, and 750.827 N, respectively. Additionally, these features all positively contributed to femoral strength prediction. In conclusion, this study provided a noninvasive, effective, and explainable method of femoral strength assessment, and it may have clinical application potential.